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Authors: Konstantinos N. Vavliakis 1 ; 2 ; Andreas L. Siailis 1 and Andreas L. Symeonidis 1

Affiliations: 1 Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, GR54124, Greece ; 2 Pharm24.gr, Dafni Lakonias, GR23057, Greece

Keyword(s): Sales Forecasting, e-Commerce, Neural Network, ARIMA, RNN.

Abstract: Sales forecasting is the process of estimating future revenue by predicting the amount of product or services a sales unit will sell in the near future. Although significant advances have been made in developing sales forecasting techniques over the past decades, the problem is so diverse and multi-dimensional that only in a few cases high accuracy predictions can be achieved. In this work, we propose a new hybrid model that is suitable for modeling linear and non-linear sales trends by combining an ARIMA (autoregressive integrated moving average) model with an LSTM (Long short-term memory) neural network. The primary focus of our work is predicting e-commerce sales, so we incorporated in our solution the value of the final sale, as it greatly affects sales in highly competitive and price-sensitive environments like e-commerce. We compare the proposed solution against three competitive solutions using a dataset coming from a real-life e-commerce store, and we show that our solution o utperforms all three competing models. (More)

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Paper citation in several formats:
Vavliakis, K.; Siailis, A. and Symeonidis, A. (2021). Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-536-4; ISSN 2184-3252, SciTePress, pages 299-306. DOI: 10.5220/0010659500003058

@conference{webist21,
author={Konstantinos N. Vavliakis. and Andreas L. Siailis. and Andreas L. Symeonidis.},
title={Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST},
year={2021},
pages={299-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010659500003058},
isbn={978-989-758-536-4},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST
TI - Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models
SN - 978-989-758-536-4
IS - 2184-3252
AU - Vavliakis, K.
AU - Siailis, A.
AU - Symeonidis, A.
PY - 2021
SP - 299
EP - 306
DO - 10.5220/0010659500003058
PB - SciTePress